Characterizing the ideal patient for treatment with inotuzumab ozogamicin for relapsed/refractory acute lymphoblastic leukemia: a systematic literature review
Bibliographic record
Abstract
Inotuzumab ozogamicin(InO) is indicated for the treatment of adults with relapsed or refractory(R/R) acute lymphoblastic leukemia (ALL). This systematic literature review (CRD42022330496) assessed outcomes bybaseline characteristics for patients with R/R ALL treated with InO to identifywhich patients may benefit most. In adherencewith PRISMA guidelines, searches were run in Embase and MEDLINE. Inclusioncriteria were real-world evidence, observational studies, and phase 2-4 trials.The Cochrane Risk of Bias tool and Newcastle-Ottawa instrument assessedquality. 34 publicationswere included; 11 described the phase 3 INO-VATE trial. Patients treated withInO who were CD22-positive, in first salvage, and eligible for subsequent hematopoieticstem cell transplant (HSCT) had improved outcomes. Reduced incidence ofveno-occlusive disease was observed in patients with normal transaminase levels and bilirubin, no priorliver disease, and who did not receive dual alkylators. The idealpatient for InO treatment has CD22-positive disease (≥20% leukemic blasts), normal liverfunction, no history of liver disease, is in first salvage, hasnot previously received HSCT, prefers outpatient treatment, or has high diseaseburden. Limitations included potentially missing publications that werenon-English, not identified in the searches, or available after the date thesearches were conducted. This systematic review was registered on theProspective Register of Systematic Reviews (PROSPERO), registration number:CRD42022330496.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".